Personalized Dynamic Attention Multi-task Learning model for document retrieval and query generation

In the actual retrieval scenario, queries from different users tend to have different retrieval intentions. Accurately understanding users’ retrieval intentions is a fundamental challenge for search engines. In this paper, we propose a Personalized Dynamic Attention Multi-task Learning model (PDAML) to solve this problem, which can clarify the user’s retrieval intention and generate a personalized document list. Specifically, we design a personalized retrieval model that can learn user retrieval preferences based on the user’s historical behavior. In addition, we propose an ad-hoc model based on multi-task learning to train query generation tasks and document retrieval tasks to enhance query representation and integrate document-aware interactive information into query representation through a dynamic attention mechanism. We can generate query representations with intent information and rank candidate documents through the interaction of the two sub-models. Experimental results on the publicly available AOL and SogouQ datasets demonstrate the effectiveness of PDAML, which improves the retrieval accuracy and personalized experience for users.